Machine learning is a general term that describes automatically setting the parameters of a system so that the system operates better. One common use for machine learning is the training of parameters for a system that predicts the behavior of objects or the relationship between objects. An example of such a system is a language model used to predict the likelihood of a sequence of words in a language.
One problem with current machine learning is that it can require a great deal of time to train a single system. In particular, systems that utilize Maximum Entropy techniques to describe the probability of some event tend to have long training times, especially if the number of different features that the system uses is large.
Conditional Maximum Entropy models have been used for a variety of natural language tasks, including Language Modeling, part-of-speech tagging, prepositional phrase attachment, parsing, word selection for machine translation, and finding sentence boundaries. Unfortunately, although maximum entropy (maxent) models can be applied very generally, the conventional training algorithm for maxent, Generalized Iterative Scaling (GIS) can be extremely slow.